code: def clusterer_ensemble_scores(
      original_labels,
      n_estimators,
      n_clusters,
      weights=None,
      return_results=False,
      reference_idx=0,
  ):
      """Function to align the raw clustering results from base estimators.
      Different from ClustererEnsemble class, this function takes in the output
      from base estimators directly without training and prediction.
  
      Parameters
      ----------
      original_labels : numpy array of shape (n_samples, n_estimators)
          The raw output from base estimators
  
      n_estimators : int
          The number of base estimators.
  
      n_clusters : int, optional (default=8)
          The number of clusters.
  
      weights : numpy array of shape (1, n_estimators)
          Estimators weights.
  
      return_results : bool, optional (default=False)
          If True, also return the aligned label matrix.
  
      reference_idx : int in range [0, n_estimators-1], optional (default=0)
          The ith base estimator used as the reference for label alignment.
  
      Returns
      -------
      aligned_labels : numpy array of shape (n_samples, n_estimators)
          The aligned label results by using reference_idx estimator as the
          reference.
  
      """
  
      original_labels = _validate_cluster_number(
          original_labels, n_clusters
      )
      alignment_mat = np.zeros([n_clusters, n_estimators])
      aligned_labels = np.copy(original_labels)
  
      for i in range(n_estimators):
          inter_mat = _intersection_mat(
              original_labels, reference_idx, i, n_clusters
          )
          index_mapping = _alignment(
              inter_mat, n_clusters, i, aligned_labels, OFFSET_FACTOR
          )
          alignment_mat[:, i] = index_mapping[:, 1]
  
      aligned_labels = aligned_labels - OFFSET_FACTOR
  
      if weights is not None:
          assert_equal(original_labels.shape[1], weights.shape[1])
  
      # equal weights if not set
      else:
          weights = np.ones([1, n_estimators])
  
      labels_by_vote = majority_vote(
          aligned_labels, n_classes=n_clusters, weights=weights
      )
      if return_results:
          return labels_by_vote.astype(int), aligned_labels.astype(int)
      else:
          return labels_by_vote.astype(int)
  
file_path: combo/combo/models/cluster_comb.py
imports: import numpy
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from numpy.testing import assert_equal
  from pyod.utils.utility import check_parameter
  from .base import BaseAggregator
  from .score_comb import majority_vote
label_desc: Use image node_id 1 for calling a global function with example usage: clusterer_ensemble_scores(original_labels, n_estimators, n_clusters, weights, return_results, reference_idx) and returns: labels_by_vote,  aligned_labels, labels_by_vote